The document discusses strategies for evaluating antivirus software programs, including testing detection rates against malware databases, assessing false positives using clean files, and examining prevention and cleaning capabilities by attempting to infect systems. It also covers documenting test results, providing developers review opportunities, and creating cross-reference lists to standardize malware naming between different antivirus products.
Fighting advanced malware using machine learning (English)FFRI, Inc.
In this paper, behavioral-based detection powered by machine learning is introduced. As the result, detection ratio is dramatically improved by comparison with traditional detection.
Needless to say that malware detection is getting harder today. Everybody knows signature-based detection reaches its limit, so that most anti-virus vendors use heuristic, behavioral and reputation-based detections altogether. About targeted attack, basically attackers use undetectable malware, so that reputation-based detection doesn't work well because it needs other victims beforehand. And it is a fact that detection ratio is not enough though we use heuristic and behavioral-based detections. In our research using the Metascan, average detection ratio of newest malware by most anti-virus scanner is about 30 %( the best is about 60 %).
By the way, heuristic and behavioral-based detections are developed by knowledge and experience of malware analyst. For example, most analysts know that following features are indicator that those programs are malicious.
- A file imports VirtualAlloc, VirtualProtect and LoadLibrary only and has a strange section name
- An entry point that does not fall within declared text or code section
- Creating remote threads into a legitimate process like explore.exe
- After unpacking, calling OpenMutex and CreateMutex to avoid multiple infections
- Register itself to auto start extension points like services and registry
- Creating a .bat file and try to delete own itself through executing the file with cmd.exe
- Setting global hook to capture keystroke using SetWindowsHookEx
Heuristic and behavioral-based detections are developed based on those pre-determined features like above. Analysts are finding those features day by day. But, this kind of work is not appropriate for human. Therefore we classified programs as malware or benign by machine learning through dynamic analysis results. Thereby, detection ratio is dramatically improved and we could recognize that which features are strongly related to malware by numeric score. And then, we could find the features which we’ve never found by this method. Finally, the outlook and challenges of this method will be tackled.
The document discusses strategies for evaluating antivirus software programs, including testing detection rates against malware databases, assessing false positives using clean files, and examining prevention and cleaning capabilities by attempting to infect systems. It also covers documenting test results, providing developers review opportunities, and creating cross-reference lists to standardize malware naming between different antivirus products.
Fighting advanced malware using machine learning (English)FFRI, Inc.
In this paper, behavioral-based detection powered by machine learning is introduced. As the result, detection ratio is dramatically improved by comparison with traditional detection.
Needless to say that malware detection is getting harder today. Everybody knows signature-based detection reaches its limit, so that most anti-virus vendors use heuristic, behavioral and reputation-based detections altogether. About targeted attack, basically attackers use undetectable malware, so that reputation-based detection doesn't work well because it needs other victims beforehand. And it is a fact that detection ratio is not enough though we use heuristic and behavioral-based detections. In our research using the Metascan, average detection ratio of newest malware by most anti-virus scanner is about 30 %( the best is about 60 %).
By the way, heuristic and behavioral-based detections are developed by knowledge and experience of malware analyst. For example, most analysts know that following features are indicator that those programs are malicious.
- A file imports VirtualAlloc, VirtualProtect and LoadLibrary only and has a strange section name
- An entry point that does not fall within declared text or code section
- Creating remote threads into a legitimate process like explore.exe
- After unpacking, calling OpenMutex and CreateMutex to avoid multiple infections
- Register itself to auto start extension points like services and registry
- Creating a .bat file and try to delete own itself through executing the file with cmd.exe
- Setting global hook to capture keystroke using SetWindowsHookEx
Heuristic and behavioral-based detections are developed based on those pre-determined features like above. Analysts are finding those features day by day. But, this kind of work is not appropriate for human. Therefore we classified programs as malware or benign by machine learning through dynamic analysis results. Thereby, detection ratio is dramatically improved and we could recognize that which features are strongly related to malware by numeric score. And then, we could find the features which we’ve never found by this method. Finally, the outlook and challenges of this method will be tackled.
An Introduction to Malware ClassificationJohn Seymour
With more than 1 million new pieces of malware released every day, security vendors are turning toward machine learning to automate threat detection. This talk aims to give new researchers the background they need for contributing to this field. We'll talk about sources for malicious PE files, consistently top-performing machine learning algorithms, extracting features, and how to prevent overfitting. (20 minute)
Measuring the Actual Security that Vendors Provide to CustomersAnthony Arrott
“There is a desperate need for new standards for today’s anti-virus products. The dominant paradigm, scanning directories of files, is focused on old and known threats, and reveals little about product efficacy in the wild.”
Williamson & Gorelik (2007)
This document discusses including security in DevOps initiatives. It recommends integrating security tools and practices into the software development lifecycle (SDLC) to build security in from the start. This includes running automated vulnerability scanning tools like ZAP and sqlmap in CI/CD pipelines. It also recommends code reviews, security testing, environment hardening, and keeping dependencies up-to-date. The goal is to shift security left and automate security practices to continuously test and deploy more secure software.
Whittaker How To Break Software Security - SoftTest IrelandDavid O'Dowd
The document discusses different approaches to software testing, specifically functional testing versus security testing. It notes that security testing requires thinking about what the software should not do rather than just what it should do. It provides examples of security bugs related to external dependencies, unanticipated user input, vulnerable design, and vulnerable implementation. It advocates using specific security testing techniques to identify these types of vulnerabilities, such as exploring how applications interact with their environment and inputs they may not anticipate. The key takeaways are to consider what should not happen with a program, understand its environment, identify worst-case scenarios, and use attacks and tools commonly used by hackers to test for security issues.
The document discusses different types of antivirus testing methods and potential ways to exploit weaknesses in those methods. It describes "wildcore" testing using real malware samples and "zoo" testing using large malware collections. It also outlines "retrospective" testing using older signature databases. The document suggests hacks like automatically signing samples, customizing settings, and detecting other antivirus products' false positives to manipulate test results. Feedback from the antivirus industry is mixed, with some condoning common practices while others find them problematic.
The document discusses how network security assessments have traditionally been done manually, which is time-consuming and error-prone. It then describes how Cisco used automation to map their entire global network infrastructure in two weeks. Several case studies are presented that show how automation can efficiently analyze network topology and access, detect vulnerabilities and policy violations, and evaluate proposed configuration changes. Automation provides a consistent, repeatable process for network security that improves visibility, prioritization of issues, and decision making.
This document outlines plans for setting up a secure cybersecurity lab with three main goals: 1) Support both Windows and Linux systems to emulate a real-world environment, 2) Be able to isolate the lab network from the campus network for exercises, and 3) Provide a mixed-use space for both security exercises and general usage. Key aspects of the lab setup include using virtual machines on physical systems to lower costs, purchasing a managed switch to isolate student workstations, and configuring older or vulnerable operating systems on student systems to simulate real-world targets.
Oh dear, your application has suddenly stopped working as expected. What should you do now?
Using techniques applicable to any php application, we'll go over what to look for and which problems to avoid when trying to determine where the problem lies. We'll show how to correctly identify and deal with problems including:
* network connectivity
* server config issues
* php config
* WSOD
* common CakePHP application errors
The document discusses the current and future states of automated malware generation and malware defense techniques. It describes how malware distribution networks currently work and trends showing rising malware samples. The future of malware defense is proposed to apply more machine learning and statistical techniques to model malware behaviors and attributes in order to handle growing sample volumes. This would involve training machine learning classifiers on features identified by human experts to classify and cluster malware more effectively.
David Parnas - Documentation Based Software Testing - SoftTest IrelandDavid O'Dowd
This document discusses documentation-based software testing and testing approaches. It advocates planning testing early and basing tests on documentation prepared throughout the design process. This allows test plans and evaluation to be determined in advance so high quality standards can be enforced on a project. The document also discusses different types of testing like black box, clear box, and grey box testing and notes that while black box testing tests against specifications, knowledge of internal structure can provide better test coverage.
This document discusses Google's approach to testing software at different levels. It defines small, medium, and large tests based on their properties. Small tests are unit tests that test individual functions and classes. Medium tests test interactions between modules on a single machine. Large tests are system or integration tests that exercise complete applications and external dependencies. The document emphasizes writing many small tests and using fakes and mocks to isolate dependencies. It also discusses strategies for dealing with flaky tests, such as automatically quarantining flaky tests. Finally, it provides an example of how large tests may work at different stages from development to production.
This document presents a technique for semantics-aware malware detection. It aims to design a malware detection algorithm that uses the semantics of instructions to identify malware even after it has been obfuscated. The technique specifies malicious behaviors as templates containing instructions, variables, and symbolic constants. It then uses a formal semantics approach and translation-validation to determine if programs are semantically equivalent and discover malicious programs despite polymorphism or metamorphism techniques used by malware writers. The strengths are robustness to code changes, while limitations include challenges with certain obfuscation techniques.
The document notes that manually analyzing malware can be time consuming and boring. MART was created to automate parts of the process such as sample acquisition, analysis using tools like Cuckoo Sandbox, and reporting. This reduces the time spent by malware analysts and allows them to focus on more complex samples. The system also aims to address limitations of virtual machine-based analysis by integrating additional techniques. Overall, MART streamlines malware analysis as a hobby while cutting costs compared to paying for commercial solutions.
Machine Learning for Malware Classification and ClusteringAshwini Almad
1) Machine learning can be used as a replacement for antivirus software by using statistical techniques to learn patterns from large malware datasets.
2) Boosted decision trees are well-suited for malware classification because they perform like a game of 20 questions to maximize discrimination between malware and benign classes.
3) Features used in machine learning models require a balance between complexity, which provides more information but less explainability, and explainability, which provides insights to analysts but may not help classification.
The document discusses unknown vulnerability management (UVM) which involves detecting vulnerabilities, including zero-days, building defenses, and deploying patches. The UVM process includes attack surface analysis through fuzz testing software, reporting issues found, and mitigating risks through patch verification and IDS rule development. Key challenges are communicating issues without leaks, reproducing bugs easily, and ensuring patches do not introduce new issues.
Watchtowers of the Internet - Source Boston 2012Stephan Chenette
Watchtowers of the Internet: Analysis of Outbound Malware Communication, Stephan Chenette, Principal Security Researcher, (@StephanChenette) & Armin Buescher, Security Researcher
With advanced malware, targeted attacks, and advanced persistent threats, it’s not IF but WHEN a persistant attacker will penetrate your network and install malware on your company’s network and desktop computers. To get the full picture of the threat landscape created by malware, our malware sandbox lab runs over 30,000 malware samples a day. Network traffic is subsequently analyzed using heuristics and machine learning techniques to statistically score any outbound communication and identify command & control, back-channel, worm-like and other types of traffic used by malware.
Our talk will focus on the setup of the lab, major malware families as well as outlier malware, and the statistics we have generated to give our audience an exposure like never before into the details of malicious outbound communication. We will provide several tips, based on our analysis to help you create a safer and more secure network.
Stephan Chenette is a principal security researcher at Websense Security Labs, specializing in research tools and next generation emerging threats. In this role, he identifies and implements exploit and malcode detection techniques.
Armin Buescher is a Security Researcher and Software Engineer experienced in strategic development of detection/prevention technologies and analysis tools. Graduated as Dipl.-Inf. (MSc) with thesis on Client Honeypot systems. Interested in academic research work and published author of security research papers.
Cyber Defense Forensic Analyst - Real World Hands-on ExamplesSandeep Kumar Seeram
The document discusses analyzing malware using static and dynamic analysis techniques. Static analysis involves examining a malware file's code and structure without executing it, using tools like disassemblers and string extractors. Dynamic analysis executes malware in a controlled environment to observe its behaviors and any changes it makes. The document then demonstrates analyzing the "Netflix Account Generator" malware using an isolated cloud sandbox, where it is observed starting child processes and making outbound network connections, suggesting it is a remote access trojan.
Exploratory testing is a systematic approach that involves designing and executing tests to learn about a system in parallel. It relies on rigorous analysis techniques and testing heuristics to discover risks. The tester dynamically adapts their approach based on insights from previous experiments to inform future tests. Exploratory testing emphasizes self-directed learning and improving testing skills over time.
This document summarizes a webinar about building custom gene expression analysis panels. It introduces the GNC Pro software for pathway analysis and systems biology. The webinar discusses starting with a seed gene list, expanding the list using interactome data from multiple sources, performing quality control checks, and finalizing the custom gene list. It provides examples of using the process for disease states and pathways. The goal is to help researchers develop the best possible gene list to analyze for their specific biology of interest.
Rahul Verma and Pradeep Soundararajan as young kids in testing in 2010 got together in Cuppa, JP Nagar, Bangalore and decided they would spend time together to help themselves and other testers. This is one of the outputs they produced. 2010 it is. Some ideas could be outdated or wrong even for 2010. Use it as a trigger to your own thought process and not as someone gave you something useful. Shared in 2019 when Rahul and Pradeep went back in memory over a beer talking about how did we get to this point of having a beer together after so many years.
Using Vuln Chaining and Other Factors for a Better Risk PerspectiveCurtis Brazzell
I introduce what I think is a new idea to track and relate vulns to each other in a data store.
In AppSec, most people understand that context is everything when it comes to assigning risk. Certain factors and other vulnerabilities, when combined together, can increase the severity of a vulnerability. Defenders and bug hunters alike help organizations understand a more accurate threat landscape from experience, but it's not something that is well documented. Join Curtis as he discuses this gap and introduces some tools and new resources for vuln chaining.
An Introduction to Malware ClassificationJohn Seymour
With more than 1 million new pieces of malware released every day, security vendors are turning toward machine learning to automate threat detection. This talk aims to give new researchers the background they need for contributing to this field. We'll talk about sources for malicious PE files, consistently top-performing machine learning algorithms, extracting features, and how to prevent overfitting. (20 minute)
Measuring the Actual Security that Vendors Provide to CustomersAnthony Arrott
“There is a desperate need for new standards for today’s anti-virus products. The dominant paradigm, scanning directories of files, is focused on old and known threats, and reveals little about product efficacy in the wild.”
Williamson & Gorelik (2007)
This document discusses including security in DevOps initiatives. It recommends integrating security tools and practices into the software development lifecycle (SDLC) to build security in from the start. This includes running automated vulnerability scanning tools like ZAP and sqlmap in CI/CD pipelines. It also recommends code reviews, security testing, environment hardening, and keeping dependencies up-to-date. The goal is to shift security left and automate security practices to continuously test and deploy more secure software.
Whittaker How To Break Software Security - SoftTest IrelandDavid O'Dowd
The document discusses different approaches to software testing, specifically functional testing versus security testing. It notes that security testing requires thinking about what the software should not do rather than just what it should do. It provides examples of security bugs related to external dependencies, unanticipated user input, vulnerable design, and vulnerable implementation. It advocates using specific security testing techniques to identify these types of vulnerabilities, such as exploring how applications interact with their environment and inputs they may not anticipate. The key takeaways are to consider what should not happen with a program, understand its environment, identify worst-case scenarios, and use attacks and tools commonly used by hackers to test for security issues.
The document discusses different types of antivirus testing methods and potential ways to exploit weaknesses in those methods. It describes "wildcore" testing using real malware samples and "zoo" testing using large malware collections. It also outlines "retrospective" testing using older signature databases. The document suggests hacks like automatically signing samples, customizing settings, and detecting other antivirus products' false positives to manipulate test results. Feedback from the antivirus industry is mixed, with some condoning common practices while others find them problematic.
The document discusses how network security assessments have traditionally been done manually, which is time-consuming and error-prone. It then describes how Cisco used automation to map their entire global network infrastructure in two weeks. Several case studies are presented that show how automation can efficiently analyze network topology and access, detect vulnerabilities and policy violations, and evaluate proposed configuration changes. Automation provides a consistent, repeatable process for network security that improves visibility, prioritization of issues, and decision making.
This document outlines plans for setting up a secure cybersecurity lab with three main goals: 1) Support both Windows and Linux systems to emulate a real-world environment, 2) Be able to isolate the lab network from the campus network for exercises, and 3) Provide a mixed-use space for both security exercises and general usage. Key aspects of the lab setup include using virtual machines on physical systems to lower costs, purchasing a managed switch to isolate student workstations, and configuring older or vulnerable operating systems on student systems to simulate real-world targets.
Oh dear, your application has suddenly stopped working as expected. What should you do now?
Using techniques applicable to any php application, we'll go over what to look for and which problems to avoid when trying to determine where the problem lies. We'll show how to correctly identify and deal with problems including:
* network connectivity
* server config issues
* php config
* WSOD
* common CakePHP application errors
The document discusses the current and future states of automated malware generation and malware defense techniques. It describes how malware distribution networks currently work and trends showing rising malware samples. The future of malware defense is proposed to apply more machine learning and statistical techniques to model malware behaviors and attributes in order to handle growing sample volumes. This would involve training machine learning classifiers on features identified by human experts to classify and cluster malware more effectively.
David Parnas - Documentation Based Software Testing - SoftTest IrelandDavid O'Dowd
This document discusses documentation-based software testing and testing approaches. It advocates planning testing early and basing tests on documentation prepared throughout the design process. This allows test plans and evaluation to be determined in advance so high quality standards can be enforced on a project. The document also discusses different types of testing like black box, clear box, and grey box testing and notes that while black box testing tests against specifications, knowledge of internal structure can provide better test coverage.
This document discusses Google's approach to testing software at different levels. It defines small, medium, and large tests based on their properties. Small tests are unit tests that test individual functions and classes. Medium tests test interactions between modules on a single machine. Large tests are system or integration tests that exercise complete applications and external dependencies. The document emphasizes writing many small tests and using fakes and mocks to isolate dependencies. It also discusses strategies for dealing with flaky tests, such as automatically quarantining flaky tests. Finally, it provides an example of how large tests may work at different stages from development to production.
This document presents a technique for semantics-aware malware detection. It aims to design a malware detection algorithm that uses the semantics of instructions to identify malware even after it has been obfuscated. The technique specifies malicious behaviors as templates containing instructions, variables, and symbolic constants. It then uses a formal semantics approach and translation-validation to determine if programs are semantically equivalent and discover malicious programs despite polymorphism or metamorphism techniques used by malware writers. The strengths are robustness to code changes, while limitations include challenges with certain obfuscation techniques.
The document notes that manually analyzing malware can be time consuming and boring. MART was created to automate parts of the process such as sample acquisition, analysis using tools like Cuckoo Sandbox, and reporting. This reduces the time spent by malware analysts and allows them to focus on more complex samples. The system also aims to address limitations of virtual machine-based analysis by integrating additional techniques. Overall, MART streamlines malware analysis as a hobby while cutting costs compared to paying for commercial solutions.
Machine Learning for Malware Classification and ClusteringAshwini Almad
1) Machine learning can be used as a replacement for antivirus software by using statistical techniques to learn patterns from large malware datasets.
2) Boosted decision trees are well-suited for malware classification because they perform like a game of 20 questions to maximize discrimination between malware and benign classes.
3) Features used in machine learning models require a balance between complexity, which provides more information but less explainability, and explainability, which provides insights to analysts but may not help classification.
The document discusses unknown vulnerability management (UVM) which involves detecting vulnerabilities, including zero-days, building defenses, and deploying patches. The UVM process includes attack surface analysis through fuzz testing software, reporting issues found, and mitigating risks through patch verification and IDS rule development. Key challenges are communicating issues without leaks, reproducing bugs easily, and ensuring patches do not introduce new issues.
Watchtowers of the Internet - Source Boston 2012Stephan Chenette
Watchtowers of the Internet: Analysis of Outbound Malware Communication, Stephan Chenette, Principal Security Researcher, (@StephanChenette) & Armin Buescher, Security Researcher
With advanced malware, targeted attacks, and advanced persistent threats, it’s not IF but WHEN a persistant attacker will penetrate your network and install malware on your company’s network and desktop computers. To get the full picture of the threat landscape created by malware, our malware sandbox lab runs over 30,000 malware samples a day. Network traffic is subsequently analyzed using heuristics and machine learning techniques to statistically score any outbound communication and identify command & control, back-channel, worm-like and other types of traffic used by malware.
Our talk will focus on the setup of the lab, major malware families as well as outlier malware, and the statistics we have generated to give our audience an exposure like never before into the details of malicious outbound communication. We will provide several tips, based on our analysis to help you create a safer and more secure network.
Stephan Chenette is a principal security researcher at Websense Security Labs, specializing in research tools and next generation emerging threats. In this role, he identifies and implements exploit and malcode detection techniques.
Armin Buescher is a Security Researcher and Software Engineer experienced in strategic development of detection/prevention technologies and analysis tools. Graduated as Dipl.-Inf. (MSc) with thesis on Client Honeypot systems. Interested in academic research work and published author of security research papers.
Cyber Defense Forensic Analyst - Real World Hands-on ExamplesSandeep Kumar Seeram
The document discusses analyzing malware using static and dynamic analysis techniques. Static analysis involves examining a malware file's code and structure without executing it, using tools like disassemblers and string extractors. Dynamic analysis executes malware in a controlled environment to observe its behaviors and any changes it makes. The document then demonstrates analyzing the "Netflix Account Generator" malware using an isolated cloud sandbox, where it is observed starting child processes and making outbound network connections, suggesting it is a remote access trojan.
Exploratory testing is a systematic approach that involves designing and executing tests to learn about a system in parallel. It relies on rigorous analysis techniques and testing heuristics to discover risks. The tester dynamically adapts their approach based on insights from previous experiments to inform future tests. Exploratory testing emphasizes self-directed learning and improving testing skills over time.
This document summarizes a webinar about building custom gene expression analysis panels. It introduces the GNC Pro software for pathway analysis and systems biology. The webinar discusses starting with a seed gene list, expanding the list using interactome data from multiple sources, performing quality control checks, and finalizing the custom gene list. It provides examples of using the process for disease states and pathways. The goal is to help researchers develop the best possible gene list to analyze for their specific biology of interest.
Rahul Verma and Pradeep Soundararajan as young kids in testing in 2010 got together in Cuppa, JP Nagar, Bangalore and decided they would spend time together to help themselves and other testers. This is one of the outputs they produced. 2010 it is. Some ideas could be outdated or wrong even for 2010. Use it as a trigger to your own thought process and not as someone gave you something useful. Shared in 2019 when Rahul and Pradeep went back in memory over a beer talking about how did we get to this point of having a beer together after so many years.
Using Vuln Chaining and Other Factors for a Better Risk PerspectiveCurtis Brazzell
I introduce what I think is a new idea to track and relate vulns to each other in a data store.
In AppSec, most people understand that context is everything when it comes to assigning risk. Certain factors and other vulnerabilities, when combined together, can increase the severity of a vulnerability. Defenders and bug hunters alike help organizations understand a more accurate threat landscape from experience, but it's not something that is well documented. Join Curtis as he discuses this gap and introduces some tools and new resources for vuln chaining.
The document discusses research issues in virus analysis. It summarizes the antivirus detection process, malware analysis process, and research issues in antivirus labs and on desktops. Some key challenges mentioned are sample collection and filtering, signature distribution at scale, prevention, detection, and recovery techniques on desktops. The document also provides an overview of the current state of antivirus technologies like heuristics, signature updates, and efforts toward automation in antivirus labs.
The document discusses the development of a forensic lab at Rochester Institute of Technology. It outlines the goals of providing hands-on experience for students to learn forensic investigation procedures and tools. Challenges in choosing appropriate tools and preserving evidence are discussed. The lab curriculum covers topics such as incident response, drive imaging, data recovery and analysis using both open-source and commercial tools. Students provided positive feedback, enjoying the practical learning experience, though some asked for more real-world case studies and more time for in-depth exploration. The document proposes expanding inter-course collaboration to continuously evolve lab materials and keep pace with new threats.
This document discusses computer forensics and outlines best practices for evidence collection, analysis, and reporting. It defines key terms like evidence, chain of custody, and imaging. It emphasizes the importance of planning, minimizing data loss, documenting all actions, and analyzing all collected data to ensure the integrity of the forensic investigation process.
This document provides an overview of next generation sequencing technologies and applications. It summarizes an upcoming webinar series on next generation sequencing and its role in cancer biology. The first webinar will provide an introduction to next generation sequencing technologies and applications and be presented by Quan Peng on April 4, 2013. The following two webinars will focus on next generation sequencing for cancer research and data analysis and be presented on April 11 and 18, 2013 respectively.
Docker in Open Science Data Analysis Challenges by Bruce HoffDocker, Inc.
Typically in predictive data analysis challenges, participants are provided a dataset and asked to make predictions. Participants include with their prediction the scripts/code used to produce it. Challenge administrators validate the winning model by reconstructing and running the source code.
Often data cannot be provided to participants directly, e.g. due to data sensitivity (data may be from living human subjects) or data size (tens of terabytes). Further, predictions must be reproducible from the code provided by particpants. Containerization is an excellent solution to these problems: Rather than providing the data to the participants, we ask the participants to provided a Dockerized "trainable" model. We run the both the training and validation phases of machine learning and guarantee reproducibility 'for free'.
We use the Docker tool suite to spin up and run servers in the cloud to process the queue of submitted containers, each essentially a batch job. This fleet can be scaled to match the level of activity in the challenge. We have used Docker successfully in our 2015 ALS Stratification Challenge and our 2015 Somatic Mutation Calling Tumour Heterogeneity (SMC-HET) Challenge, and are starting an implementation for our 2016 Digitial Mammography Challenge.
The document discusses data-driven approaches to optimizing software testing processes at Microsoft. It describes how historical test and code data can be analyzed to determine which tests are most valuable and cost-effective to run, in order to reduce total test execution time without negatively impacting code quality. Simulation results on Windows 8.1 data show the potential for significant test reduction (up to 60%) while maintaining bug finding ability. This could improve development processes by lowering machine costs and increasing developer satisfaction.
Predict Conference: Data Analytics for Digital Forensics and CybersecurityMark Scanlon
Information overload is one of the biggest problems facing professionals working in the fields of Digital Forensics and Cybersecurity. The sheer volume of cases requiring digital forensic analysis in law enforcement agencies throughout the world is outstripping the capacities of digital forensic laboratories. This has resulted in huge digital evidence backlogs becoming commonplace and cases being ruled upon in court without the inclusion of potentially pertinent information, which is sitting idle in some evidence store. As is commonly relayed in the media, the frequency of cyberattacks being faced by governments, law enforcement agencies, and industry is increasing, alongside the sophistication of the techniques used. Current rules-based network intrusion detection systems are predominantly based on historic, known threat vectors and result in a very high amount of false positive alerts being generated. Intelligent, real-time, automated data processing and event categorisation is one solution that shows great promise to combat this information overload.
This document summarizes the results of an anti-virus test conducted in March 2012. 20 anti-virus products were tested on their ability to detect malware. G Data had the highest detection rate at 99.7%, while AhnLab had the lowest at 94%. Microsoft had the fewest false positives at 0, while Webroot had the most at 428. Based on detection rates and false positives, products received awards of Advanced+, Advanced, Standard or Tested. G Data, AVIRA and Kaspersky received Advanced+.
Applications of genetic algorithms to malware detection and creationUltraUploader
This document summarizes and analyzes previous research on applying genetic algorithms to malware detection and creation. Section 2 summarizes a paper that compared the performance of genetic algorithm-based classifiers to non-genetic classifiers for detecting malware. It found genetic algorithms performed comparably to other methods in classification accuracy but with lower processing overhead. Sections 3 and 4 summarize papers applying genetic algorithms to optimize parameters for real-time malware detection and to evolve malware signatures similar to antibodies. Section 5 discusses using genetic algorithms to evolve malware. The document analyzes the effectiveness of genetic algorithms for malware detection tasks and issues around using them to evolve malware.
Software testing is a process used to identify issues and ensure quality in developed software. It involves techniques like unit testing of individual code components, integration testing of interface between components, and system testing of the full application. While exhaustive testing of all possible inputs is not feasible due to time constraints, techniques like equivalence partitioning, boundary value analysis, and error guessing help prioritize test cases. The goal is to thoroughly test the most important and error-prone areas with the time available.
Showing How Security Has (And Hasn't) Improved, After Ten Years Of TryingDan Kaminsky
The document discusses the results of fuzz testing software from 2000-2010 to analyze how software security has improved over the last decade. The testing involved fuzzing four file formats (Office, PDF, etc.) across 18 programs from different years. This resulted in over 175,000 crashes. Analysis found over 900 unique bugs. Later versions had fewer exploitable bugs, indicating improving code quality. The results provide a potential "fuzzmark" metric for software security improvements, though comparisons across formats require more controls. The testing process and challenges ensuring data integrity are also outlined.
The document discusses different types of software testing including unit testing. It provides an overview of unit testing, how to write good unit tests, examples of unit testing FizzBuzz and Rock Paper Scissors Lizard Spock, and how to do unit testing on Android including mocking and test-driven development. It also briefly discusses test automation and using Calabash for cross-platform mobile app testing.
2010-03-31 - VU Amsterdam - Experiences testing safety critical systemsJaap van Ekris
1) Testing safety critical systems is challenging because software often contains errors and failures can have catastrophic consequences, so systems must be designed and tested to extremely high standards of reliability.
2) The document discusses standards like IEC 61508 that provide requirements for safety integrity levels and risk management in developing safety critical systems.
3) Rigorous verification techniques are needed including reviews, static analysis, unit testing with high code coverage, integration testing of components, system testing of full environments, and acceptance testing of real systems.
This document summarizes a keynote presentation about challenges in bioinformatics software development and proposed solutions. Some of the key points made include: 1) bioinformatics software development involves multiple disciplines including computer science, software engineering, statistics, and biology, each with different priorities; 2) there is a massive proliferation of bioinformatics software packages that leads to many difficult choices for researchers; 3) proposed solutions include developing software in a more modular and automated way, using common benchmarks and protocols to evaluate tools, and focusing on reproducibility and usability.
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Discover the exhilarating world of snowshoeing through our presentation, highlighting the challenges faced by beginners. From physical exertion to technical finesse and braving harsh winter conditions, each step in the snow brings new obstacles and unforgettable adventures. Embrace the challenge and conquer the winter wonderland with confidence!
What Outdoor Adventures Await Young Adults in Montreal's Surrounding NatureSpade & Palacio Tours
Experience Montreal's vibrant culture and thrilling outdoor adventures. From hiking scenic trails at Mont-Saint-Bruno to kayaking the Saint Lawrence River, there's something for every adventurous young adult. Explore street art, camp under the stars, and immerse yourself in nature's beauty just beyond the city's bustling streets.
Our excursions in tahiti offer stunning lagoon tours, vibrant marine life encounters, and cultural experiences. We ensure unforgettable adventures amidst breathtaking landscapes and serene waters. For more information, mail us at tracey@uniquetahiti.com.
Nature of the task 1. write a paragraph about your trip to dubai and what ar...solutionaia
1. write a paragraph about your trip to dubai and what are the facts responsible for the heavy rainfall in dubai that caused havec ?
2. mention any five major tourist attaction of dubai
Mathematics: a student a visit her family her father converted Rs. 1,15,000 inr currency for dubai airport theexpedite in the trip is given below
1. Curreny name of India and dubai
2. Conversions amount
3. Total Converted amount
4. cost of food
5. cost of sightseeing
6. cost of shoping
7. cost of saving
Prepare for cold weather rafting with proper gear: layering for warmth, insulated headgear, gloves, waterproof footwear, and essential accessories like sunglasses and sunscreen. Prioritize safety with a life jacket and maintain gear for optimal performance. Stay warm, dry, and ready for adventure on the rapids!
The Ultimate Travel Guide to Hawaii Island Hopping in 2024adventuressabifn
island hopping in Hawaii. This magical place offers a number of experiences, as each island has its own charm. Do you love adventure? Then, hike through volcanoes or kayak along an amazing coastline. If you want relaxation, then Hawaii is perfect because it has the most amazing beaches. In this blog, I will help you make an itinerary for your Hawaii Island Hopping. Every island offers something special. The Big Island offers some of the most perfect volcanoes as well as stargazing. Maui will pamper you with beaches and luxury. Kauai has some of the most lush rainforests with dramatic cliffs, and Oahu can offer a taste of city life and historical significance.
Golden Gate Bridge: Magnificent Architecture in San Francisco | CIO Women Mag...CIOWomenMagazine
The famous suspension bridge connects the city of San Francisco to Marin County in California. Golden Gate Bridge carries both U.S. Route 101 and California State Route 1 across the strait and is about one mile wide. In this article, we will explore the history and significance that have shaped the iconic monument it is known as today.
Un viaje a Buenos Aires y sus alrededoresJudy Hochberg
A travelogue of my recent trip to Argentina, most to Buenos Aires, but including excursion to Iguazú waterfalls, Tigre, and Colonia del Sacramento in Uruguay
Ibiza, situated in the Balearic Islands, stands out as a destination that encompasses everything: stunning landscapes, hidden gems to explore, a vibrant social scene, rich cultural life, and exceptional gastronomy. Opting for ‘Ibiza Rent A Boat’ to experience an unforgettable vacation on the White Island is certainly worthwhile, prompting a deeper exploration of the unique and fascinating aspects of Ibiza.
How To Change Your Name On American Airlines Aadvantage.pptxedqour001namechange
American Airlines permits passengers to change/correct names on their AAdvantage account. Also, you can request a name change both online via a web portal and offline over the phone. For further information on how to change your name on American Airlines Advantage, get in touch with the airline’s customer service. Also, you can reach out to a consolidation desk at +1-866-738-0741 for quick assistance.
How do I plan a Kilimanjaro Climb?
Planning to climb Mount Kilimanjaro is an exciting yet detailed process. Here’s a step-by-step guide to help you prepare for this incredible adventure.